School of Mathematics, Statistics & Actuarial Science

About

Jim is the Head of the School's Statistics group and the Deputy Head of School. He serves as the School's Director of Research and chairs our Research and Innovation Committee.

Contact Information

Address

Room 349

Office hours: Tu 14:30-15:30/Th 10:30-11:30

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Publications

Also view these in the Kent Academic Repository

Article
Griffin, J. and Leisen, F. (2017). Compound Random Measures and their use in Bayesian nonparametrics. Journal of the Royal Statistical Society Series B-Statistical Methodology [Online] 79:525-545. Available at: http://dx.doi.org/10.1111/rssb.12176.
Griffin, J. and Leisen, F. (2017). Modelling and computation using NCoRM mixtures for density regression. Bayesian Analysis.
Griffin, J. and Brown, P. (2017). Hierarchical Shrinkage Priors for Regression Models. Bayesian Analysis [Online] 12:135-159. Available at: http://dx.doi.org/10.1214/15-BA990.
Griffin, J., Kalli, M. and Steel, M. (2017). Discussion of "Nonparametric Bayesian Inference in Applications": Bayesian nonparametric methods in econometrics. Statistical Methods & Applications [Online]. Available at: https://doi.org/10.1007/s10260-017-0384-0.
Griffin, J. and Sakaria, D. (2016). On efficient Bayesian inference for models with stochastic volatility. Econometrics and Statistics [Online] 3:23-33. Available at: https://doi.org/10.1016/j.ecosta.2016.08.002.
Griffin, J. and Kalli, M. (2015). Flexible Modelling of Dependence in Volatility Processes. Journal of Business and Economic Statistics [Online] 33:102-113. Available at: http://amstat.tandfonline.com/doi/abs/10.1080/07350015.2014.925457#.VOSycldJ54E.
Holmes, C. et al. (2015). Two-sample Bayesian nonparametric hypothesis testing. Bayesian Analysis [Online] 10:297-320. Available at: http://dx.doi.org/10.1214/14-BA914.
Griffin, J. (2015). Sequential Monte Carlo methods for mixtures with normalized random measures with independent increments priors. Statistics and Computing [Online] 27:131-145. Available at: https://doi.org/10.1007/s11222-015-9612-3.
Kalli, M. and Griffin, J. (2014). Time-varying sparsity in dynamic regression models. Journal of Econometrics [Online] 178:779-793. Available at: http://dx.doi.org/10.1016/j.jeconom.2013.10.012.
Griffin, J. (2014). An adaptive truncation method for inference in Bayesian nonparametric models. Statistics and Computing [Online] 26:423-441. Available at: http://dx.doi.org/10.1007/s11222-014-9519-4.
Lamnisos, D., Griffin, J. and Steel, M. (2013). Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models. Journal of Computational and Graphical Statistics [Online] 22:729-748. Available at: http://amstat.tandfonline.com/doi/pdf/10.1080/10618600.2012.694756.
Kolossiatis, M., Griffin, J. and Steel, M. (2013). On Bayesian nonparametric modelling of two correlated distributions. Statistics and Computing [Online] 23:1-15. Available at: http://dx.doi.org/10.1007/s11222-011-9283-7.
Griffin, J. and Delatola, E. (2013). A Bayesian semiparametric model for volatility with a leverage effect. Computational Statistics and Data Analysis [Online] 60:97-110. Available at: http://www.sciencedirect.com/science/article/pii/S016794731200391X.
Griffin, J. and Walker, S. (2013). On adaptive Metropolis-Hastings method. Statistics and Computing [Online] 23:123-134. Available at: http://link.springer.com/article/10.1007%2Fs11222-011-9296-2.
Griffin, J. and Brown, P. (2013). Some Priors for Sparse Regression Modelling. Bayesian Analysis [Online] 8:691-702. Available at: http://ba.stat.cmu.edu/journal/2013/vol08/issue03/griffin.pdf.
Griffin, J., Kolossiatis, M. and Steel, M. (2013). Comparing distributions by using dependent normalized random-measure mixtures . Journal of the Royal Statistical Society: Series B (Statistical Methodology) [Online] 75:499-529. Available at: http://dx.doi.org/10.1111/rssb.12002.
Lamnisos, D., Griffin, J. and Steel, M. (2012). Cross-validation prior choice in Bayesian probit regression with many covariates. Statistics and Computing [Online] 22:359-373. Available at: http://dx.doi.org/10.1007/s11222-011-9228-1.
Griffin, J. and Brown, P. (2012). Structuring Shrinkage: Some Correlated Priors for Regression. Biometrika [Online] 99:481-487. Available at: http://dx.doi.org/10.1093/biomet/asr082.
Kirk, P. et al. (2012). Bayesian correlated clustering of integrated multiple datasets. Bioinformatics [Online] 28:3290-3297. Available at: http://bioinformatics.oxfordjournals.org/content/28/24/3290.
Griffin, J. and Oomen, R. (2011). Covariance measurement in the presence of non-synchronous trading and market microstructure noise. Journal of Econometrics [Online] 160:58-68. Available at: http://dx.doi.org/10.1016/j.jeconom.2010.03.015.
Griffin, J. (2011). Bayesian clustering of distributions in stochastic frontier analysis. Journal of Productivity Analysis [Online] 36:275-283. Available at: http://dx.doi.org/10.1007/s11123-011-0213-7.
Griffin, J. and Steel, M. (2011). Stick-Breaking Autoregressive Processes. Journal of Econometrics [Online] 162:383-396. Available at: http://dx.doi.org/10.1016/j.jeconom.2011.03.001.
Griffin, J. (2011). The Ornstein-Uhlenbeck Dirichlet Process and other time-varying processes for Bayesian nonparametric inference. Journal of Statistical Planning and Inference [Online] 141:3648-3664. Available at: http://dx.doi.org/10.1016/j.jspi.2011.05.019.
Griffin, J. and Walker, S. (2011). Posterior Simulation of Normalized Random Measure Mixtures. Journal of Computational and Graphical Statistics 20:241-259.
Griffin, J. (2011). Inference in Infinite Superpositions of Non-Gaussian Ornstein–Uhlenbeck Processes Using Bayesian Nonparametic Methods. Journal of Financial Econometrics [Online] 9:519-549. Available at: http://dx.doi.org/10.1093/jjfinec/nbq027.
Griffin, J. and Brown, P. (2011). Bayesian hyper-lassos with non-convex penalization. Australian and New Zealand Journal of Statistics [Online] 53:423-442. Available at: http://dx.doi.org/10.1111/j.1467-842X.2011.00641.x.
Delatola, E. and Griffin, J. (2011). Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility. Bayesian Analysis [Online] 6:901-926. Available at: http://dx.doi.org/10.1214/11-BA632.
Kalli, M., Griffin, J. and Walker, S. (2011). Slice Sampling Mixture Models. Statistics and Computing [Online] 21:93-105. Available at: http://dx.doi.org/10.1007/s11222-009-9150-y.
Kolossiatis, M., Griffin, J. and Steel, M. (2011). Modeling overdispersion with the Normalized Tempered Stable distribution. Computational Statistics and Data Analysis [Online] 55:2288-2301. Available at: http://dx.doi.org/10.1016/j.csda.2011.01.016.
Griffin, J. and Steel, M. (2010). Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes. Computational Statistics and Data Analysis [Online] Online:2594-2608. Available at: http://dx.doi.org/10.1016/j.csda.2009.06.008.
Savage, R. et al. (2010). Discovering Transcriptional Modules from Bayesian Data Fusion. Bioinformatics [Online] 26:1158-1167. Available at: http://dx.doi.org/10.1093/bioinformatics/btq210.
Griffin, J. and Brown, P. (2010). Inference with normal-gamma prior distributions in regression problems. Bayesian Analysis [Online] 5:171-188. Available at: http://dx.doi.org/10.1214/10-BA507.
Griffin, J. and Steel, M. (2010). Bayesian Nonparametric Modelling with the Dirichlet Process Regression Smoother. Statistica Sinica [Online] 20:1507-1527. Available at: http://www3.stat.sinica.edu.tw/statistica/j20n4/j20n48/j20n48.html.
Griffin, J. (2010). Default priors for density estimation with mixture models. Bayesian Analysis [Online] 5:45-64. Available at: http://dx.doi.org/10.1214/10-BA502.
Lamnisos, D., Griffin, J. and Steel, M. (2009). Transdimensional Sampling Algorithms for Bayesian Variable Selection in Classification Problems With Many More Variables Than Observations. Journal of Computational and Graphical Statistics [Online] 18:592-612. Available at: http://dx.doi.org/10.1198/jcgs.2009.08027.
Griffin, J. and Oomen, R. (2008). Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time? Econometric Reviews [Online] 27:230-253. Available at: http://dx.doi.org/10.1080/07474930701873341.
Griffin, J. and Steel, M. (2008). Flexible mixture modelling of stochastic frontiers. Journal of Productivity Analysis [Online] 29:33-50. Available at: http://dx.doi.org/10.1007/s11123-007-0064-4.
Griffin, J. and Steel, M. (2007). Bayesian Stochastic Frontier Analysis Using WinBUGS. Journal of Productivity Analysis [Online] 27:163-176. Available at: http://dx.doi.org/10.1007/s11123-007-0033-y.
Griffin, J. and Steel, M. (2006). Order-Based Dependent Dirichlet Processes. Journal of the American Statistical Association [Online] 101:179-94. Available at: http://dx.doi.org/10.1198/016214505000000727.
Griffin, J. and Steel, M. (2006). Inference with non-Gaussian Ornstein–Uhlenbeck processes for stochastic volatility. Journal of Econometrics [Online] 134:605-644. Available at: http://dx.doi.org/10.1016/j.jeconom.2005.07.007.
Griffin, J. and Steel, M. (2004). Semiparametric Bayesian inference for stochastic frontier models. Journal of Econometrics [Online] 123:121-152. Available at: http://dx.doi.org/10.1016/j.jeconom.2003.11.001.
Book section
Griffin, J., Quintana, F. and Steel, M. (2011). Flexible and Nonparametric Methods. in: Koop, G., van Dijk, H. and Geweke, J. eds. The Oxford Handbook of Bayesian Econometrics. Oxford University Press.
Griffin, J. and Holmes, C. (2010). Computational issues arising in Bayesian nonparametric hierarchical models. in: Hjort, N. L. et al. eds. Bayesian Nonparametrics. Cambridge University Press.
Monograph
Hodges, S. et al. (2001). Non-Gaussian Ornstein-Uhlenbeck-based Models and Some of their Uses in Financial Economics - Discussion. BLACKWELL PUBLISHING LTD. Available at: http://dx.doi.org/10.1111/1467-9868.00282.
Conference or workshop item
Hoggart, C. and Griffin, J. (2001). A Bayesian Partition Model for Customer Attrition. in: ISBA 2000: The Sixth World Meeting of the International Society for Bayesian Analysis. pp. 223-232.
Research report (external)
Kalli, M., Griffin, J. and Walker, S. (2008). Slice Sampling Mixture Models. Centre for Health Services Studies.
Total publications in KAR: 46 [See all in KAR]
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Research Interests

  • Bayesian nonparametric methods including:
    • the development of dependent nonparametric priors such as compound random measures (CoRM)
    • computational methods for nonparametric modelling such as a pseudo-marginal MCMC sampler for normalized CoRM mixtures, sequential Monte Carlo methods for normalized random measures or adaptive truncation methods
  • The application of nonparametric and semiparametric methods to economic and financial data including:
    • the development of a Bayesian nonparametric vector autoregression model for multivariate time series
    • the analysis of high-frequency data using Bayesian realized volatility
    • the construction of quantile time series models for financial data
    • the modelling of financial data with flexible stochastic volatility models and jumps
  • Sparse Bayesian methods for regression problems with many variables including:
    • efficient computation using adaptive Monte Carlo methods
    • the use of shrinkage priors in models with interactions
    • applications in bioinformatics and medicine.
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Teaching

MA639: Time Series Modelling and Simulation
MA781: Practical Multivariate Analysis
MA885: Stochastic Processes and Time Series
MA886: Modelling of Time-Dependent Data and Financial Econometrics
MA889: Analysis of Large Data Sets
MA5501: Applied Statistical Modelling 1
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School of Mathematics, Statistics and Actuarial Science (SMSAS), Sibson Building, Parkwood Road, Canterbury, CT2 7FS

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Last Updated: 16/10/2017